CN113465613A - Map matching optimization method for tunnel network positioning in urban rail transit - Google Patents
Map matching optimization method for tunnel network positioning in urban rail transit Download PDFInfo
- Publication number
- CN113465613A CN113465613A CN202110832876.2A CN202110832876A CN113465613A CN 113465613 A CN113465613 A CN 113465613A CN 202110832876 A CN202110832876 A CN 202110832876A CN 113465613 A CN113465613 A CN 113465613A
- Authority
- CN
- China
- Prior art keywords
- matching
- positioning
- road
- tunnel network
- rail transit
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000000034 method Methods 0.000 title claims abstract description 27
- 238000005457 optimization Methods 0.000 title claims abstract description 15
- 238000004422 calculation algorithm Methods 0.000 claims abstract description 24
- 230000007704 transition Effects 0.000 claims abstract description 17
- 238000004364 calculation method Methods 0.000 claims abstract description 9
- 239000011159 matrix material Substances 0.000 claims abstract description 8
- 238000005070 sampling Methods 0.000 claims description 9
- 230000008859 change Effects 0.000 claims description 5
- 230000008569 process Effects 0.000 claims description 5
- 238000000342 Monte Carlo simulation Methods 0.000 abstract description 9
- 238000000605 extraction Methods 0.000 description 5
- 239000002131 composite material Substances 0.000 description 2
- 238000005094 computer simulation Methods 0.000 description 2
- 238000004088 simulation Methods 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000000903 blocking effect Effects 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000001914 filtration Methods 0.000 description 1
- 230000002068 genetic effect Effects 0.000 description 1
- 238000013178 mathematical model Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000011524 similarity measure Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/26—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network
- G01C21/28—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network with correlation of data from several navigational instruments
- G01C21/30—Map- or contour-matching
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
- G06F30/27—Design optimisation, verification or simulation using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model
Abstract
The invention relates to the technical field of positioning and navigation, in particular to a map matching optimization method for tunnel network positioning in urban rail transit. The method comprises the following steps: A. acquiring tunnel network route data; B. carrying out positioning point matching road section set calculation on road sections near the positioning points; C. generating a markov chain required for markov chain monte carlo using a Gibbs sampler; D. constructing a state model and initializing a state transition probability matrix; E. and calculating an expected value of the state model by using Monte Carlo integral, wherein the finally obtained expected value is the matching probability of each road section, the maximum probability is the matching road section, and the map matching is completed. The map matching algorithm based on the Markov chain Monte Carlo method model and Bayesian optimization is adopted, so that the problems of low positioning matching precision and slow time efficiency in urban rail transit maps with dense tunnel networks and complex topological relations in the prior art can be solved.
Description
Technical Field
The invention relates to the technical field of positioning and navigation, in particular to a map matching optimization method for tunnel network positioning in urban rail transit.
Background
With the increasingly wide application of navigation systems, people put higher demands on the positioning accuracy of the navigation systems. Due to the existence of a plurality of unavoidable factors, the method for positioning by using the Satellite Navigation System (GNSS) technology has a large error.
In order to solve the problem of poor positioning accuracy, some map matching algorithms are generally used for correction. Existing map matching algorithms can be broadly divided into 4 types: an algorithm based on geometric information, an algorithm based on topological relation, an algorithm based on probability statistics and a composite matching algorithm.
In addition, there are some improved algorithms established by other mathematical models: 1. the algorithm based on the geometric information does not consider the road topological structure, and error matching is easy to cause. 2. The algorithm based on the topological relation is easily influenced by factors such as noise and data sparsity, and the problem of complex tunnel network route matching is difficult to solve. 3. Although the accuracy rate of a composite matching algorithm such as Kalman filtering, a D-S evidence theory and the like is high, the error rate is high during low-frequency sampling. 4. The algorithm based on probability statistics relates to complex formula proving and deduction, and the stability of the algorithm is poor.
In the prior art, similar to the present invention, a map matching algorithm based on a hidden markov model is used, and the algorithm performs calculation by substituting a driving distance and a link parameter with the hidden markov model, then performs parameter learning by using a standard forward and backward algorithm, and performs link prediction by using a Viterbi algorithm or other algorithms to obtain an optimal link, thereby completing map matching.
The existing map matching algorithm based on the hidden Markov model and the genetic algorithm needs to input positioning point and route data, but the modern urban rail transit line network has huge data, dense tunnel networks and complex topological relations, and consumes time when searching road sections, inflection points and route intersections on the tunnel networks. When the correction candidate points of the positioning points are selected, the minimum distance from the positioning points to each road section needs to be calculated, the existing urban rail transit lines are complex, a plurality of tunnels exist, and the timeliness is greatly reduced when each road section is subjected to matching calculation. Finally, when the transition probability of the candidate matching points before and after modeling is carried out, insufficient matching features or wrong feature selection can significantly influence the description capability of the model on the matching context, so that the matching accuracy is significantly reduced.
For urban rail transit maps with dense networks and complex traffic network topological relations, an algorithm which can accurately position each tunnel network route and is quick in positioning time is needed.
Disclosure of Invention
The invention provides a map matching optimization method for tunnel network positioning in urban rail transit, which uses a map matching algorithm based on a Markov chain Monte Carlo method model and carrying out Bayesian optimization and can solve the problems of low positioning matching precision and slow time efficiency in urban rail transit maps with dense tunnel networks and complex topological relations in the prior art.
The technical scheme adopted by the invention is as follows:
a map matching optimization method for tunnel network positioning in urban rail transit comprises the following steps:
A. acquiring tunnel network route data;
B. carrying out positioning point matching road section set calculation on road sections near the positioning points;
C. generating a markov chain required for markov chain monte carlo using a Gibbs sampler;
D. constructing a state model and initializing a state transition probability matrix;
E. and calculating an expected value of the state model by using Monte Carlo integral, wherein the finally obtained expected value is the matching probability of each road section, the maximum probability is the matching road section, and the map matching is completed.
The step A is specifically to divide the tunnel network route into blocks according to the latitude and longitude range, divide the tunnel network route into 100 blocks of 10 x 10 equally according to the latitude and longitude range, divide each block into road segment hash blocks and inflection point hash blocks of 10 x 10 equally according to the latitude and longitude range, obtain the data of the tunnel network route by adopting a hash algorithm, read the road segments in each block of the area when the data is read, place the corresponding road segment hash blocks according to the latitude and longitude of the road segments, and then sequentially read the inflection points in the road segments.
In the step B, the selection mode of the adjacent road sections is to calculate the shortest distance from each road section to the positioning point, and then delete the road sections of which the shortest distance is two times of the standard deviation larger than the average value.
In the step D, the state transition probability matrix gives the probability of the transition of the positioning point between the candidate matching road sections, for one positioning point and the matching road section thereof, and the next positioning point and the matching road section, the shortest distance between the point and the road is changed, the transition probability with small change is larger, and the transition probability with small change is smaller.
In step E, the monte carlo integral formula is as follows:
namely, it is
Wherein x istAnd generating samples for the sampling model, wherein n is the total number of samples in the sampling process, and m is the number of samples when the generated Markov chain reaches a plateau.
Topological relation: refers to the interrelationship between each spatial data satisfying the topological geometry principle. I.e., adjacency, association, containment and connectivity relationships between entities represented by nodes, arc segments and polygons. Such as: the relationship of the dots to the adjacent dots, the relationship of the dots to the surface, the relationship of the lines to the surface, the relationship of the surfaces to the surface, and the like.
Tunnel network: the network is formed by urban rail transit underground track lines.
Markov chain monte carlo method: MCMC for short is a Monte Carlo method (Monte Carlo) that is simulated by a computer under the bayes theory framework. The method introduces a Markov (Markov) process into Monte Carlo simulation, realizes dynamic simulation of sampling distribution changing along with the simulation, and makes up the defect that the traditional Monte Carlo integral can only be statically simulated.
The map matching method comprises the following steps: sub-regions with invariant or significant features are extracted from the reference map, or a method is used to search for regions in the matched map that are similar to the template, using known ground control points as templates. And when the matching similarity measure reaches the maximum and exceeds a preset threshold value, judging that a correct matching position is found. The method is a map positioning and deviation rectifying method.
Urban rail transit: urban rail transit is a vehicle transportation system which adopts a rail structure for bearing and guiding, and is a public transportation mode of conveying passenger flow of a considerable scale in a train or single vehicle mode by arranging a fully-closed or partially-closed special rail line according to the requirements of the overall planning of urban traffic.
The technical scheme provided by the invention has the beneficial effects that:
the method comprises the steps of processing urban rail transit tunnel network route data by using a Hash technology, reading road sections in each area during data reading, and placing corresponding road section Hash blocks according to the longitude and latitude of the road sections; and inflection points in the road section are read in sequence, so that the data acquisition and reading time is greatly reduced, and the positioning time is shortened. The Markov chain Monte Carlo method can be used for dynamic simulation, the convergence rate is greatly increased, the calculation time is shortened, the calculation efficiency is improved, and the positioning accuracy and the timeliness are greatly improved.
Compared with the prior art, the invention has the following characteristics:
1. the Markov chain Monte Carlo method is used for calculating, so that the calculation time is greatly reduced, and the calculation efficiency is improved.
2. And the data of the tunnel network is processed by using the hash technology, so that the data processing and reading time is reduced.
3. And a Markov chain Monte Carlo method is used for establishing a model, so that the positioning accuracy is improved.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
Fig. 1 is a flowchart of a method for map matching optimization of tunnel network positioning in urban rail transit according to the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, embodiments of the present invention will be described in detail with reference to the accompanying drawings.
Example one
As shown in fig. 1, the map matching optimization method for tunnel network positioning in urban rail transit according to the embodiment includes the following steps:
1. firstly, tunnel network route data are obtained, and it is time-consuming to find the road sections and inflection points of each route, so that the tunnel network data of urban rail transit are processed by adopting a Hash technology. As urban rail transit routes of all places are different, the method is suitable for all places and carries out blocking according to latitude and longitude ranges. The tunnel network route is equally divided into 100 areas of 10 x 10 according to the latitude and longitude range, and each area is equally divided into a road segment hash block and an inflection point hash block of 10 x 10 according to the latitude and longitude range.
2. The markov chain required for the markov chain monte carlo method was generated using a Gibbs sampler.
How many road segments are from the location point, the state vector generated by the Gibbs sampler is an m-dimensional vector, where the nth sample can be expressed as
(1) let n equal to 0, initial state point
The probability that each road segment is taken as an initial road segment is the shortest distance from each road segment to the positioning point divided by the maximum value of the shortest distances in all the road segments. If the minimum number of iterations required for the markov chain to converge is k and the length of the expected simulation sequence is g, the total number of samples is t ═ k + g.
(2) From the complete conditional distribution
Middle extraction
From the complete conditional distribution
Middle extraction
By analogy, finish
The extraction of (1).
(3) Repeating the step (2) until i is t +1
3. Constructing a state model, initializing a state transition probability matrix: the state transition probability matrix gives the probability of the transition of the positioning point between the candidate matching road sections, for one positioning point and the matching road section thereof, and the next positioning point and the matching road section, the shortest distance between the point and the road changes, the transition probability with small change is larger, and the transition probability with small change is smaller.
4. Calculating the expectation of the above model using Monte Carlo integrals, i.e.
Wherein x istAnd generating samples for the sampling model, wherein n is the total number of samples in the sampling process, and m is the number of samples when the generated Markov chain reaches a plateau.
The state transition probability matrix constructed is the Markov chain required for Gibbs sampler generation, i.e., for the full conditional distribution
Middle extraction
The extraction of each step of (1).
And finally, obtaining the expected value which is the matching probability of each road section, wherein the maximum probability is the matching road section, and completing map matching.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.
Claims (5)
1. A map matching optimization method for tunnel network positioning in urban rail transit comprises the following steps:
A. acquiring tunnel network route data;
B. carrying out positioning point matching road section set calculation on road sections near the positioning points;
C. generating a markov chain required for markov chain monte carlo using a Gibbs sampler;
D. constructing a state model and initializing a state transition probability matrix;
E. and calculating an expected value of the state model by using Monte Carlo integral, wherein the finally obtained expected value is the matching probability of each road section, the maximum probability is the matching road section, and the map matching is completed.
2. The map matching optimization method for tunnel network positioning in urban rail transit according to claim 1, wherein the step a is specifically to block the tunnel network route according to the latitude and longitude range, equally divide the tunnel network route into 100 blocks of 10 × 10 according to the latitude and longitude range, equally divide each block into a road segment hash block and an inflection point hash block of 10 × 10 according to the latitude and longitude range, obtain the tunnel network route data by using a hash algorithm, read the road segment in each block when the data is read, place the corresponding road segment hash block according to the latitude and longitude of the road segment, and sequentially read the inflection points in the road segment.
3. The map matching optimization method for tunnel network positioning in urban rail transit according to claim 1, wherein in step B, the selection of the nearby road segments is performed by calculating the shortest distance from each road segment to the positioning point, and then deleting the road segments with the shortest distance greater than the average value by two times of the standard deviation.
4. The map matching optimization method for tunnel network positioning in urban rail transit as claimed in claim 1, wherein in said step D, the state transition probability matrix gives the probability of the transition of the positioning point between the candidate matching road segments, and for one positioning point and its matching road segment, and the next positioning point and matching road segment, the shortest distance between the point and the road changes, and the transition probability with small change is larger, and vice versa.
5. The map matching optimization method for tunnel network positioning in urban rail transit according to claim 1, wherein in step E, the monte carlo integral formula is as follows:
namely, it is
,
Wherein xtAnd generating samples for the sampling model, wherein n is the total number of samples in the sampling process, and m is the number of samples when the generated Markov chain reaches a plateau.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110832876.2A CN113465613B (en) | 2021-07-22 | 2021-07-22 | Map matching optimization method for tunnel network positioning in urban rail transit |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110832876.2A CN113465613B (en) | 2021-07-22 | 2021-07-22 | Map matching optimization method for tunnel network positioning in urban rail transit |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN113465613A true CN113465613A (en) | 2021-10-01 |
| CN113465613B CN113465613B (en) | 2023-12-26 |
Family
ID=77881815
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110832876.2A Active CN113465613B (en) | 2021-07-22 | 2021-07-22 | Map matching optimization method for tunnel network positioning in urban rail transit |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113465613B (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116862737A (en) * | 2023-06-30 | 2023-10-10 | 数字宁波科技有限公司 | Public facility site selection method based on simulated annealing and Monte Carlo methods |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20110313648A1 (en) * | 2010-06-16 | 2011-12-22 | Microsoft Corporation | Probabilistic Map Matching From A Plurality Of Observational And Contextual Factors |
| CN108061556A (en) * | 2017-10-18 | 2018-05-22 | 广州傲胜机器人科技有限公司 | A kind of map-matching method based on hidden Markov model |
| CN109979194A (en) * | 2019-03-07 | 2019-07-05 | 长安大学 | Heavy vehicle ramp based on Markov chain is creeped Activity recognition method |
| EP3552904A1 (en) * | 2018-04-10 | 2019-10-16 | Bayerische Motoren Werke Aktiengesellschaft | Method, device and computer program product for predicting the development of a traffic scene involving several participants |
| CN110631594A (en) * | 2019-10-24 | 2019-12-31 | 成都大成均图科技有限公司 | Offline map matching method and system based on complex trajectory network partitioning model |
| CN111065893A (en) * | 2017-06-01 | 2020-04-24 | 罗伯特·博世有限公司 | Method and device for creating a lane-accurate road map |
| CN111343585A (en) * | 2020-02-28 | 2020-06-26 | 重庆邮电大学 | Mobile user track map matching method based on hidden Markov model |
| CN112015835A (en) * | 2020-08-13 | 2020-12-01 | 安徽师范大学 | Geohash compressed map matching method |
-
2021
- 2021-07-22 CN CN202110832876.2A patent/CN113465613B/en active Active
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20110313648A1 (en) * | 2010-06-16 | 2011-12-22 | Microsoft Corporation | Probabilistic Map Matching From A Plurality Of Observational And Contextual Factors |
| CN111065893A (en) * | 2017-06-01 | 2020-04-24 | 罗伯特·博世有限公司 | Method and device for creating a lane-accurate road map |
| CN108061556A (en) * | 2017-10-18 | 2018-05-22 | 广州傲胜机器人科技有限公司 | A kind of map-matching method based on hidden Markov model |
| EP3552904A1 (en) * | 2018-04-10 | 2019-10-16 | Bayerische Motoren Werke Aktiengesellschaft | Method, device and computer program product for predicting the development of a traffic scene involving several participants |
| CN109979194A (en) * | 2019-03-07 | 2019-07-05 | 长安大学 | Heavy vehicle ramp based on Markov chain is creeped Activity recognition method |
| CN110631594A (en) * | 2019-10-24 | 2019-12-31 | 成都大成均图科技有限公司 | Offline map matching method and system based on complex trajectory network partitioning model |
| CN111343585A (en) * | 2020-02-28 | 2020-06-26 | 重庆邮电大学 | Mobile user track map matching method based on hidden Markov model |
| CN112015835A (en) * | 2020-08-13 | 2020-12-01 | 安徽师范大学 | Geohash compressed map matching method |
Non-Patent Citations (2)
| Title |
|---|
| BODA HAN等: "A Map Matching Algorithm for Complex Road", 2018 IEEE INTERNATIONAL CONFERENCE ON BIG DATA AND SMART COMPUTING, pages 426 - 431 * |
| 毛江云;吴昊;孙未未;: "路网空间下基于马尔可夫决策过程的异常车辆轨迹检测算法", 计算机学报, vol. 41, no. 08, pages 1928 - 1942 * |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116862737A (en) * | 2023-06-30 | 2023-10-10 | 数字宁波科技有限公司 | Public facility site selection method based on simulated annealing and Monte Carlo methods |
Also Published As
| Publication number | Publication date |
|---|---|
| CN113465613B (en) | 2023-12-26 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109670277B (en) | Travel time prediction method based on multi-mode data fusion and multi-model integration | |
| CN110260870B (en) | Map matching method, device, equipment and medium based on hidden Markov model | |
| CN107103754B (en) | Road traffic condition prediction method and system | |
| Pu et al. | A three‐dimensional distance transform for optimizing constrained mountain railway alignments | |
| CN112015835B (en) | Geohash compressed map matching method | |
| CN113065594B (en) | Road network extraction method and device based on Beidou data and remote sensing image fusion | |
| CN106227726B (en) | Path extraction method based on vehicle track data | |
| Huang et al. | Automatic generation of road maps from low quality GPS trajectory data via structure learning | |
| CN113989451B (en) | High-precision map construction method and device and electronic equipment | |
| CN109993064B (en) | Method for extracting connection path between road network nodes in picture | |
| CN105091889A (en) | Hotspot path determination method and hotspot path determination equipment | |
| CN113723715B (en) | Method, system, equipment and storage medium for automatically matching public transport network with road network | |
| CN115586557B (en) | Vehicle driving track deviation correcting method and device based on road network data | |
| Qiu et al. | Automatic extraction of road networks from GPS traces | |
| CN110986965A (en) | Low-frequency floating vehicle track data road network matching method based on hidden Markov model | |
| CN114626169A (en) | Traffic network optimization method, device, equipment, readable storage medium and product | |
| CN113932821A (en) | Track map matching method based on continuous window average direction features | |
| CN113465613A (en) | Map matching optimization method for tunnel network positioning in urban rail transit | |
| CN115265555B (en) | Map matching correction method and system based on hidden Markov multi-noise perception | |
| Blazquez et al. | An instance-specific parameter tuning approach using fuzzy logic for a post-processing topological map-matching algorithm | |
| Shigezumi et al. | A fast algorithm for matching planar maps with minimum Fréchet distances | |
| CN114664104A (en) | Road network matching method and device | |
| Wu et al. | A Vehicle Map-matching Algorithm based on Measure Fuzzy Sorting. | |
| Fan et al. | Lane-Level Road Map Construction considering Vehicle Lane-Changing Behavior | |
| KR101475359B1 (en) | Method for generating multi-scale model for network data of digital topographic map |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |